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Lund jet plane for Higgs tagging
by Charanjit K. Khosa
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Submission summary
| Authors (as registered SciPost users): | Charanjit Kaur Khosa |
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| Preprint Link: | https://arxiv.org/abs/2110.15135v2 (pdf) |
| Date accepted: | 2022-02-28 |
| Date submitted: | 2022-01-19 09:58 |
| Submitted by: | Khosa, Charanjit Kaur |
| Submitted to: | SciPost Physics Proceedings |
| Proceedings issue: | 50th International Symposium on Multiparticle Dynamics (ISMD2021) |
| Ontological classification | |
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| Academic field: | Physics |
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| Approaches: | Computational, Phenomenological |
Abstract
We study the boosted Higgs tagging using the Lund jet plane. The convolutional neural network is used for the Lund images data set to classify hadronically decaying Higgs from the QCD background. We consider $H\to b \bar{b}$ and $H \to gg$ decay for moderate and high Higgs transverse momentum and compare the performance with the cut based approach using the jet color ring observable. The approach using Lund plane images provides good tagging efficiency for all the cases.
Published as SciPost Phys. Proc. 10, 011 (2022)